A fully automatic method for vascular tortuosity feature extraction in the supra-aortic region: unraveling possibilities in stroke treatment planning.

Vascular tortuosity of supra-aortic vessels is widely considered one of the main reasons for failure and delays in endovascular treatment of large vessel occlusion in patients with acute ischemic stroke. Characterization of tortuosity is a challenging task due to the lack of objective, robust and effective analysis tools. We present a fully automatic method for arterial segmentation, vessel labelling and tortuosity feature extraction applied to the supra-aortic region. A sample of 566 computed tomography angiography scans from acute ischemic stroke patients (aged 74.8 ± 12.9, 51.0% females) were used for training, validation and testing of a segmentation module based on a U-Net architecture (162 cases) and a vessel labelling module powered by a graph U-Net (566 cases). Successively, 30 cases were processed for testing of a tortuosity feature extraction module. Measurements obtained through automatic processing were compared to manual annotations from two observers for a thorough validation of the method. The proposed feature extraction method presented similar performance to the inter-rater variability observed in the measurement of 33 geometrical and morphological features of the arterial anatomy in the supra-aortic region. This system will contribute to the development of more complex models to advance the treatment of stroke by adding immediate automation, objectivity, repeatability and robustness to the vascular tortuosity characterization of patients.

Reliability of the early syndromic diagnosis in adults with new-onset epileptic seizures: A retrospective study of 116 patients attended in the emergency room.

PURPOSE: New-onset seizures (NOS) are a common reason for emergency department (ED) consultations. Decisions regarding treatment and further examinations are made based on the initial evaluation. We aimed to evaluate the reliability of the early syndromic diagnosis in NOS and find predictive factors to establish a consistent early diagnosis based on the semiology and prompt supplementary examinations. METHODS: We recruited patients attended in our ED for NOS over 2 years (2014-2015), excluding patients with a loss of consciousness of suspected non-epileptic origin. All patients were assessed by a neurologist. A baseline diagnosis was established according to clinical findings and neuroimaging/EEG data. Over 1 year of follow-up in our Epilepsy Unit, a definite diagnosis was made based on clinical progress and further examinations. RESULTS: 116 patients were recruited (mean age 56.5 ±â€¯22.1 years; 50% women). 47% were seizures of unknown cause. The concordance index between the baseline and definite diagnosis was κ = 0.662 (the diagnosis changed during follow-up in 25% of patients). Focal epilepsy of unknown cause was the baseline diagnosis that most often changed at follow-up (diagnostic change, 41.2%; p < 0.001). Lesions detected on CT-scanning and EEG abnormalities predicted the final diagnosis with the greatest accuracy (p = 0.009 and p = 0.026, respectively). Pathological findings in the MRI studies performed and seizure recurrence were not key factors for diagnostic changes. CONCLUSION: Despite prompt examinations, the baseline epilepsy diagnosis changes within a short time period in 25% of patients. The presence of neuroimaging lesions and EEG abnormalities was associated with the greatest diagnostic accuracy in these cases.

[Progressive multifocal leukoencephalopathy and non-specific immune dysfunction]. / Leucoencefalopatia multifocal progresiva y disfuncion inmune no especificada.

TITLE: Leucoencefalopatia multifocal progresiva y disfuncion inmune no especificada.